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Exploratory research is "the preliminary research to clarify the exact nature of the problem to be solved." It is used to ensure additional research is taken into consideration during an experiment as well as determining research priorities, collecting data and honing in on certain subjects which may be difficult to take note of without exploratory research.
It can be exploratory, descriptive, or explanatory; however, explanatory research is the most common. [ citation needed ] Basic research generates new ideas, principles, and theories, which may not be immediately utilized but nonetheless form the basis of progress and development in different fields.
Exploratory research, on the other hand, seeks to generate a posteriori hypotheses by examining a data-set and looking for potential relations between variables. It is also possible to have an idea about a relation between variables but to lack knowledge of the direction and strength of the relation.
The goal of the research process is to produce new knowledge or deepen understanding of a topic or issue. This process takes three main forms (although, as previously discussed, the boundaries between them may be obscure): Exploratory research, which helps to identify and define a problem or question.
Exploratory data analysis is an analysis technique to analyze and investigate the data set and summarize the main characteristics of the dataset. Main advantage of EDA is providing the data visualization of data after conducting the analysis.
In addition, the conceptualizing of descriptive research (categorization or taxonomy) precedes the hypotheses of explanatory research. [2] ( For a discussion of how the underlying conceptualization of exploratory research, descriptive research and explanatory research fit together, see: Conceptual framework.)
Use of the phrase "working hypothesis" goes back to at least the 1850s. [7]Charles Sanders Peirce came to hold that an explanatory hypothesis is not only justifiable as a tentative conclusion by its plausibility (by which he meant its naturalness and economy of explanation), [8] but also justifiable as a starting point by the broader promise that the hypothesis holds for research.
Causal analysis is the field of experimental design and statistical analysis pertaining to establishing cause and effect. [1] [2] Exploratory causal analysis (ECA), also known as data causality or causal discovery [3] is the use of statistical algorithms to infer associations in observed data sets that are potentially causal under strict assumptions.